2014-09-21

Marine Conflicts

human uses vs. endangered species

Marine Spatial Planning

Source: Crowder et al. (2006) Resolving Mismatches in U.S. Ocean Governance. Science

Spatial Decision Support System

Data from Many Platforms

Questions (themes)

  • How to combine data from many platforms to best predict distribution and abundance of species? (disperate data)

  • How do these distributions change over time, seasonally and trending with climate change? (distributions and time)

  • What environmental covariates best predict where and when these animals are distributed? (distributions and environment)

  • How do we effectively capture and integrate uncertainty for these distributions into decision making? (uncertainty)

  • Once we can best describe the distribution of these species in space and time, how can we integrate this information into spatial decision frameworks? (decision frameworks)

    • for siting

    • for routing

Chapters: Data to Decision

  1. Robust, Dynamic Distribution Models
    • Combine plane and boat observation data (disperate data)
    • Distance from Gulf Stream (distributions and environment)
    • Predicting with Uncertainty in Measurement and/or Gap-filled Environment (uncertainty)
  2. Predicting Seasonal Migration (distributions and time)
  3. Probabilistic Range Maps (disperate data, decision frameworks, uncertainty)
  4. Decision Mapping (decision frameworks, uncertainty)
  5. Conservation Routing (decision frameworks)

1. Robust, Dynamic Distribution Models

Baseline SDM: Boat + Plane

Source: Best BD, PN Halpin et al (2012) Online Cetacean Habitat Modeling System for the U.S. East Coast and Gulf of Mexico. Endangered Species Research

Dynamic Variables from Satellite

Eddies from AVISO          Fronts from Pathfinder / GHRSST

Dynamic Vars from Ocean Models

Hybrid Coordinate Ocean Model (HYCOM)

  • pros: 1/12 °, cloud-free, 3D, forecast
  • cons: modeled, since 2003, physical only

Forecast Whales using Ocean Models

using ROMS to Oct, Nov (predicting from July)

Robust Comparison

  • Presence < Presence-Absence < Density?
  • Regression (GLM, GAM) vs Machine Learning (Maxent, BRT)
  • Predictive Performance vs Ecological Interpretation
  • Space Time Lags
  • Scaling Effects

Space vs Time

           Processes                                     Observations

2. Predicting Seasonal Migration

Grey Whale Migration

Migration Model

Migration Model

Migration Model

3. Probabilistic Range Maps

Range Map

Eubalaena glacialis

Source: IUCN

IUCN Range Maps Applied

Source: Schipper et al. (2008) The Status of the World's Land and Marine Mammals: Diversity, Threat, and Knowledge. Science

AquaMaps Environmental Envelope

Source: Eubalaena glacialis from AquaMaps.org (Ready et al. 2010)

AquaMaps Environmental Envelope

Relative Environmental Suitability

Source: Kaschner et al. (2006)

Ocean Health Index: Species

Species status based on extinction risk and species richness based on range maps from IUCN and AquaMaps (Selig et al., 2013).

Probabilistic Range Maps

Combine:

  • \(Y\): Occurrences for presence-only observation

  • \(R\): Range map from expert opinion

  • \(E\): "Effort"" proxy from all "Cetacea" occurrences

…

Probabilistic Range Maps

Combine:

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  • Environment:

    • \(sst\): sea-surface temperature

    • \(depth\): bathymetric depth

    • \(d2shore\): distance to shore

Bayesian State-Space Model

\[ \operatorname{p}(\boldsymbol{\lambda}, \boldsymbol{\beta}, \sigma^2, z | \boldsymbol{y}, \boldsymbol{W}, \boldsymbol{E}, \boldsymbol{R}) \alpha \\ \operatorname{Pois}(\boldsymbol{y}, \boldsymbol{E} \boldsymbol{\lambda}) \\ \operatorname{N_5}(\operatorname{ln} \boldsymbol{\lambda}, \boldsymbol{W} \boldsymbol{\beta}, \sigma^2 \boldsymbol{I_5}) \\ \operatorname{Bin}(\boldsymbol{R} │ 1, 1 - exp(-z \boldsymbol{\lambda}) )^{.5} \\ \operatorname{N_5}(\boldsymbol{\beta} │ \boldsymbol{\beta}_p, \boldsymbol{V}_p ) \\ \operatorname{IG}(\sigma^2 │ s_1, s_2) \]

prior densities:

  • \(N_5(\boldsymbol{\beta}_p, \boldsymbol{V}_p)\)
  • \(IG(s_1, s_2)\)



hyperparameters:

  • \(\boldsymbol{\beta}_p = [0,0,0,0,0]\)
  • \(\boldsymbol{V}_p = 1000 \boldsymbol{I}_5\)
  • \(s_1 = s_2 = 0.1\)

Right Whale Estimated Range

4. Decision Mapping

Habitat

Error

Weights

decision p(0-0.5) p(0.5-1)
go 0 3
no go 1 0

Weights associating a decided action with the integrated probability of encounter as a simple step function.

Weights

decision p(0-0.5) p(0.5-1)
go 0 3
no go 1 0
  • conservation risk of operating (go) and encountering whales, vs.
  • opportunity loss of not operating (no go) and not encountering whales

Integrated Probabilities

Cost Maps per Decision

Decision Map

Per pixel, choose decision which minimizes risk-loss function.

5. Conservation Routing

Vessel Routes

Whale areas (left) and proposed routes (right) in BC.

Integrate Marine Mammal Distributions

  • Using density spatial models for 9 marine mammal species in BC from Raincoast surveys 2004-2008

  • Composite risk map derived per pixel (\(i\)) across \(n\) species (\(s\)) by summing relative density (\(z_i\)), which is based on the pixel values' (\(x_i\)) deviation (\(\sigma_s\)) from mean density (\(\mu_s\)), and multiplying by conservation status (\(w_s\)): \[ z_{i,s} = \frac{ x_{i,s} - \mu_s }{ \sigma_s } \\ Z_i = \frac{ \sum_{s=1}^{n} z_{i,s} w_s }{ n } \]

Conservation Status (\(w_s\))

Composite Risk Map

Routes for Oil Tankers

Routes for Cruise Ships

Further Application

Boston Harbor Rerouting for Right Whales



Global Traffic

Sources:
Left: Ward-Geiger et al. (2005) Characterization of Ship Traffic in Right Whale Critical Habitat. Coastal Management
Right: Halpern et al. (2008) A Global Map of Human Impact on Marine Ecosystems. Science

Backup Slides

Acknowledgements

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Backup Slides

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